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Intelligent Cars
Nikhil M. Chakravarthy
CSE 6362
Spring 2003
Dr. Lawrence B. Holder, Jr.

Intelligent Environments

1
Purpose






Investigate the motivation of adding
Intelligence to a car.
Explore problems and solutions.
Survey the current state of research.
Identify future research trends.

Intelligent Environments

2
Outline






Definitions / Motivation
Design Goals
Problems / Solutions - Theory
Current Industry Solutions
Future Trend

Intelligent Environments

3
Definitions
Intelligence






An intelligent, incorporeal being, especially an
angel.
The capacity to acquire and apply knowledge.

Artificial





Not genuine or natural.
Brought about or caused by sociopolitical or
other human-generated forces or influences.

Intelligent Environments

4
Definitions


Artificial Intelligence




The ability of a computer or other machine
to perform those activities that are normally
thought to require intelligence.
The ability of a man made machine to
acquire and apply knowledge.

Intelligent Environments

5
Motivation






Traffic accidents.
Military operations.
Improve efficiency.
Technical challenge.
The LAW.

Intelligent Environments

6
Role Models: Benny and
Herbie

Intelligent Environments

7
Design Goals




Increase Safety.
Improve Operational Efficiency.
Enhance Driving Experience.

Intelligent Environments

8
Driver Operations


Speed Control







Ignition.
Accelerate.
Cruise.
Decelerate.
Stop.
Backup.
Intelligent Environments

9
Driver Operations


Direction





Turn left / right.
Go Straight.

Signals




Signal turns.
Turn Lights on / off.
Sound Horn.
Intelligent Environments

10
Driver Operations


Climate





Activate Wipers.
Open / Close Windows.
Open / Close Vents.
Activate Heater / AC / Fan.

Intelligent Environments

11
Driver Operations


Maintenance






Refuel.
Wash.
Service.

Abnormal Conditions




Breakdown.
Accident.
Theft.

Intelligent Environments

12
Occupant Safety


Collision Warning






Blind spot.
Pedestrian.
Roll Over.

Collision Avoidance




Steering.
Brakes.
Throttle.

Intelligent Environments

13
Occupant Comfort


Driver Assistance




Adaptive cruise control.

Vehicle Automation



Autonomous / Co-operative
Low Speed Automation

Intelligent Environments

14
Issues


Vision






Night
Bad Weather
Corners / Up Hill

Object



Stationary / In Motion
Direction / Speed
Intelligent Environments

15
Solutions





Machine Vision
Radar
GPS + Digital Maps
Sensors

Intelligent Environments

16
Solutions : by-product


‘Sensored’ Roads.



Speed Limit Signs.
Lane Markings.




Magnetic Referencing.

Road Signs.

Intelligent Environments

17
Research Prototypes







Partners for Advanced Transit and
Highways.
Vision-Based Intelligent Navigator.
Distinguishing Objects Using Laser
Radar and Vision.
“Smarter Car”.


Programmed Intelligence.
Intelligent Environments

18
Emergency Vehicle
Maneuvers and Control Laws






High-priority transit to emergency
vehicles.
Free-flowing and Stopped traffic.
Automated Highway Systems.
California Partners for Advanced Transit
and Highways (PATH).

Intelligent Environments

19
PATH Architecture.

Intelligent Environments

20
Vision-Based Intelligent
Navigator

Intelligent Environments

21
State-transition Graph

Intelligent Environments

22
Distinguishing Objects Using
Laser Radar and Vision



Scanning Laser Radar (SLR).
White Lane Markers.




Image Processing.

Objects




Vehicle
Delineator
Sign
Intelligent Environments

23
Distinguishing the Types of
Objects

Intelligent Environments

24
Fuzzy Logic + Neural Net =
“Smarter Car”

Intelligent Environments

25
Intelligence Vendors






Motorola
IBM
Philips
Bosch
…

Intelligent Environments

26
Motorola Digital DNA




Automobiles contain 200 to 450
semiconductors worth approximately
$165 (Selantek, 1998).
By 2001, the content is expected to be
worth up to $1,500 per vehicle.

Intelligent Environments

27
Motorola Digital DNA


FlexRay protocol.





DaimlerChrysler and BMW

Adapting to the User.
Intelligence in Silicon.

Intelligent Environments

28
Motorola mobileGT™


“The mobileGT™ platform from Motorola is a
complete system and alliance, enabling the
latest, customized driver information
technology. It's a solution providing
automakers and tier-one manufacturers a
single recognized platform from the
automotive semiconductor leader. It's a
solution supported by the mobileGT alliance,
the major players in the business. With its
single 32-bit PowerPC architecture, ultrareliable real-time OS, and open, scalable
Java™ framework …”
Intelligent Environments

29
Motorola mobileGT™







Speech Recognition.
Graphical User Interface (GUI).
Wireless Communications.
GPS Navigation.
Digital Radio.
Web, and Email.
Intelligent Environments

30
Motorola mobileGT™






Remote Keyless Entry (RKE) systems.
Vehicle immobilization systems.
Passive entry systems.
Tire Pressure Monitoring System.
Anti-Lock Braking Systems.

Intelligent Environments

31
Motorola eSensor™


DNA Detection System.







Binding properties of DNA and RNA.
Electronic circuit element.
Detectable electronic signal.
Disposable biochip cartridges, detection
reagents, electronic biochip reader, software
and protocols.
Convenient, economically feasible.
Intelligent Environments

32
IBM




Preventive vehicle diagnostics.
IBM Blue Octane.
Multimedia.


Digital Music.

Intelligent Environments

33
Consumers








Toyota
Volvo
BMW
Lexus
Nissan
Honda
Hyundai
Intelligent Environments

34
Intelligent …






Cruise Control
Headlights
Air Bags
Navigation
Body Color

Intelligent Environments

35
Intelligent …






Doors
Mirrors
Locks
Tires
Temperature Control

Intelligent Environments

36
Intelligent …






Steering
Seats
Speed
Entertainment
Air Flow Control

Intelligent Environments

37
Smart Airbags


“This fall, more than a third of new cars must, by
federal mandate, be able to sense the difference
between an adult occupant, a child and an empty
seat. Airbags would then only inflate as much as
needed. Weight and tension sensors under seats and
in seatbelts are the first step, but Siemens, TRW and
Motorola are developing lasers, 3-D cameras and
electrical fields that can determine occupants'
position as well as their size. "The existing
technology can determine if someone's in a seat,"
notes TRW engineer Roger McCurdy, "but the real
value will be when airbags determine when someone
is out of position -- that's the root cause of injuries. " ’’
– Popular Science April 2003
Intelligent Environments

38
Smart Airbags

A ceiling-mounted sensor "sees" who's in the car and inflates airbags to the
appropriate size. Illustration by Garry Marshall, Popular Science April 2003.
Intelligent Environments

39
Future: Riding Cars

Intelligent Environments

40
Lexus Appeal

Intelligent Environments

41
The NAME is …

Intelligent Environments

42
Losers




Emergency Road Side Infrastructure.
Insurance.
Government.




Speeding Tickets.

Artificial Intelligence.


Programmed vs. Learning

Intelligent Environments

43
Summery






Fascination for Intelligent Cars.
Problems and Solutions.
Commercial Solutions.
Technological Infrastructure.
Future Research Trends.

Intelligent Environments

44
Questions?

Intelligent Environments

45
References




Bishop, “A Survey of Intelligent Vehicle Applications
Worldwide”, Proceedings of the IEEE Intelligent
Vehicles Symposium, 2000.
Toy, C.; Leung, K.; Alvarez, L.; Horowitz, R.,
“Emergency vehicle maneuvers and control laws for
automated highway systems”, Page(s): 109-119,
IEEE Transactions on Intelligent Transportation
Systems, Jun 2002, Vol.3, Issue 2

Intelligent Environments

46
References




Kato, S.; Tsugawa, S.; Tokuda, K.; Matsui, T.; Fujii,
H., “Vehicle control algorithms for cooperative driving
with automated vehicles and intervehicle
communications”, Page(s): 155- 161, IEEE
Transactions on Intelligent Transportation Systems,
Sep 2002, Vol.3, Issue 3
Shimomura, N.; Fujimoto, K.; Oki, T.; Muro, H., “An
algorithm for distinguishing the types of objects on
the road using laser radar and vision”, Page(s): 189195, IEEE Transactions on Intelligent Transportation
Systems, Sep 2002, Vol.3, Issue 3
Intelligent Environments

47
References




Embrechts, M.J.; DiCesare, F.; Luetzelschwab,
M.J.; , “Fuzzy logic and neural net control for the
“Smarter Car“ ”, Systems, Man and Cybernetics,
1995. Page(s): 371 -376, IEEE International
Conference on 'Intelligent Systems for the 21st
Century'., Volume: 1, 22-25 Oct 1995
Miura, J.; Itoh, M.; Shirai, Y., “Toward vision-based
intelligent navigator: its concept and prototype”,
Page(s): 136- 146, IEEE Transactions on Intelligent
Transportation Systems, Jun 2002, Vol.3, Issue 2

Intelligent Environments

48
References


Moite, S., “How smart can a car be?”, Page(s): 277
-279, Proceedings of the Intelligent Vehicles '92
Symposium., 29 Jun-1 Jul 1992

Intelligent Environments

49
Web References
















http://www.motorola.com/mot/documents/0,1028,123,00.pdf
http://www.businessweek.com/adsections/smartcars/smcaroads.htm
http://www.popsci.com/popsci/auto/article/0,12543,434957,00.html
http://www.motorola.com/lifesciences/esensor/tech_overview.html
http://www.islandnet.com/~kpolsson/forsale/dis136.jpg
http://images.amazon.com/images/P/630440123X.01.LZZZZZZZ.jpg
http://www.barchetta.cc/All.Ferraris/images/0412/james-bond-a-1.jpg
http://www.killermovies.com/images/movies/bond_die1_001.jpg
http://dictionary.reference.com/
http://www.spielberg-dreamworks.com/minorityreport/presskit/Tom_Car.jpg
http://gamingasylum.topcities.com/screens/movies/minority2.jpg
http://ffmedia.ign.com/filmforce/image/haraldbelkerdesign_minorityreportlexus.jpg
http://e-www.motorola.com/webapp/sps/site/overview.jsp?
nodeId=02M0ylfWcbfM0yrBwp3h#block
http://www.studioillustrators.com/Illustrations/Cartoon%20car.jpg
Intelligent Environments

50

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Chakravarthy

Editor's Notes

  1. Good morning. Talk on intelligent cars.
  2. Think up the impossible. Reduce accidents due to negligence like sleep, bad vision. Reduce fuel consumption. Because it is there. Eliminate the brain behind the wheel.
  3. 1974 herbie. Benny the cab 1988
  4. Traffic Accidents. Theft will become car-napping. Fuel cost. Traffic congestion.
  5. relies on bioelectronics
  6. No Accidents. No Tickets. No Insurance.